Paper Info
Title
What Is Beautiful Is Not Always Good: Influence Of Machine Learning-Derived Photo Attractiveness On Intention To Initiate Social Interactions In Mobile Dating Applications
Abstract
The popularity of mobile dating applications has reconstructed how people initiate new romantic relationships. Photo attractiveness, the most prominent information provided in the online dating context before social interactions, has attracted considerable attention but reached inconsistent conclusions. By considering the location, gender, and attractiveness difference between each dyad of participants (hunter and target) together, this study attempts to re-examine the contingent impact of physical attractiveness in initiating social interactions. Multi-methods (machine-learning and panel logistic regression) were used to empirically analyse the large-scale field data. The results show that: (1) A target's photo attractiveness can promote a hunter's willingness to leave a message; (2) The impact of photo attractiveness will be attenuated when two users have a larger location difference; (3) Male users are more likely to be impacted by photo attractiveness in their social interactions than female users; (4) A hunter with low attractiveness is inclined to initiate social interactions with a more attractive target; whereas a hunter with high attractiveness does not. This study deepens the theoretical implications in relevant fields, provides new methodology insight, and offers personalised marketing strategies for mobile dating applications.
Year
DOI
Venue
2021
10.1080/09540091.2020.1814204
CONNECTION SCIENCE
Keywords
DocType
Volume
Photo attractiveness, social interaction, mobile dating application, machine learning
Journal
33
Issue
ISSN
Citations 
2
0954-0091
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ping Gao100.34
Xiaolun Wang2101.82
Chen Hong32111.66
Weihui Dai411519.79
Hong Ling511811.94